Multivariate moment based extreme value index estimators

Matias Heikkilä (), Yves Dominicy () and Pauliina Ilmonen ()
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Matias Heikkilä: Aalto University School of Science
Yves Dominicy: Université libre de Bruxelles
Pauliina Ilmonen: Aalto University School of Science

Computational Statistics, 2017, vol. 32, issue 4, No 13, 1513 pages

Abstract: Abstract Modeling extreme events is of paramount importance in various areas of science—biostatistics, climatology, finance, geology, and telecommunications, to name a few. Most of these application areas involve multivariate data. Estimation of the extreme value index plays a crucial role in modeling rare events. There is an affine invariant multivariate generalization of the well known Hill estimator—the separating Hill estimator. However, the Hill estimator is only suitable for heavy tailed distributions. As in the case of the separating multivariate Hill estimator, we consider estimation of the extreme value index under the assumptions of multivariate ellipticity and independent identically distributed observations. We provide affine invariant multivariate generalizations of the moment estimator and the mixed moment estimator. These estimators are suitable for both light and heavy tailed distributions. Asymptotic properties of the new extreme value index estimators are derived under multivariate elliptical distribution with known location and scatter. The effect of replacing true location and scatter by estimates is examined in a thorough simulation study. We also consider two data examples: one financial application and one meteorological application.

Keywords: Elliptical distribution; Moment estimator; Mixed moment estimator (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00180-016-0706-y

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